Article Figures & Data
Figures
- Figure 1.
Axon guidance phenotypes and construct schematics. A–G, Confocal images of the various axon guidance phenotypes found in frazzled LOF specimens. Midlines are shown in dotted lines. A, Wild-type-appearing GFs and terminals (arrowheads). B, A single left GF forms a bilateral terminal (arrowheads). C, Schematic of the GF system. The GF somata are labeled green, and the axons extend along the midline to form terminals that synapse onto ipsilateral TTMn partners in black. D, The missing left GF from B can be seen in the brain (arrow), but its axon does not leave the brain. The right GF extends an axon seen in B. E, Rarely occurring GFs that do not form traditional bends toward their TTMn partners. F, Disrupted axons in GFs driving UAS-HA-FraΔP2. G, Neither GF grows out of the brain. H, The frequency of different axon guidance phenotypes for each genotype tested. I, Diagram showing the structural differences between the UAS constructs used in our experiments. The extracellular domain for each construct contains four Ig C2 repeats (orange) and six fibronectin III repeats (blue), while the ICD consists of the conserved P1, P2, and P3 domains (green). UAS-FraE1354A contains an HA tag that does not influence synaptic function (seen in Extended Data Fig. 1-1).
- Figure 2.
Recording from the GF System. A, The GF System (GFS) relays signals from the brain to the jump muscles in the thorax. We insert stimulating electrodes through the eyes to extracellularly stimulate the GF and insert recording electrodes in the jump muscle to record latency and response frequency for the circuit (see Materials and Methods). B, Schematic of the GF system, with stimulating electrodes in the brain and recording electrodes placed extracellularly in the TTM. C, Latencies are recorded 10 times with 1 s intervals between stimuli. Wild type is defined by latency averages under 1.00 ms. When we stimulate the GFs 10 times in 10 s at 100 Hz, wild-type samples have a consistent response frequency averaging over 90%. The top panels are from heterozygous frazzled control sibling samples and are wild type. In contrast, the bottom panels are frazzled LOF mutants, which perform significantly worse in comparison (missing responses indicated by the X).
- Figure 3.
Structure of GF axons when driving different UAS constructs in a frazzled LOF background. Each row has images from an individual sample for a given genotype. A representation of each Frazzled construct is seen in Figure 1I. First column (A, D, G, J): Compressed z-stack maximum projection image of the GF terminals labeled with GFP and shaking-B(neural+16) gap junctions labeled with shaking-B antibody, which binds to all shaking-B isoforms. The dotted line marks the PSI region and the anterior limit of the unit terminals. Second column (B, E, H, K): Volume reconstructions of the corresponding GF terminals posterior to the PSI region. The reconstruction generates volumes for the GF terminals and the gap junction antibodies within the terminals, which are included for each sample. Third column (C, F, I, L, O): Genotype of the samples in each row and that sample’s latency and response frequency average.
- Figure 4.
Comparison of latency, response frequency, and gap junction antibody proportion in GF terminals for all genotypes tested. A, Latency comparison for all genotypes tested. Each point is the latency average of 10 trials from one side (unit terminal) of one specimen. B, We measured GF terminal and gap junction antibody volume by generating a 3D rendering of Z-stack confocal images using the IMARIS software. We plotted the percent of a GF terminal occupied by gap junction antibody for each genotype tested. C, Left- and right-side response frequency average for each fly of each genotype tested. D, Response frequency comparison for all genotypes tested. Each point is the response average for a unit terminal stimulated 10 times at 100 Hz. Center lines (A, B, D) show the medians; box limits indicate the 25th and 75th percentiles as determined by the R software; whiskers extend 1.5 times the interquartile range from the 25th and 75th percentiles; crosses represent sample means; data points are plotted as open circles.
- Figure 5.
Modeling the GF→TTMn synapse effectively simulates responses to stimulation when altering gap junction proportions in terminals. A, Our model uses compartments to simulate the GF System. A stimulus is introduced in Compartment 0 and is propagated through subsequent compartments that make up the GF model. Between Compartments 2 and 3, we placed a simulated electrochemical synapse and varied the amount of gap junction present at the synapse to alter responses to stimulation. An action potential is then produced in Compartment 3, where response latency and frequency are recorded. B, Top panel, The recordings from Compartment 3 can be seen for wild type in red and frazzled LOF in orange. Latency is calculated as the difference in time between the peaks of Compartment 0, shown in black, and Compartment 3. B, Bottom panel, Response frequency is measured by the number of action potentials recorded for 100 Hz stimulation wild-type (red) and frazzled LOF (orange). C, Gap junction conductance in the model is modulated by a sigmoid transformation. In wild-type flies, the amount of gap junction protein present in the GF terminals is, on average, 10% of the total volume of the terminal. In a shak-B2 mutant, gap junctions are absent from the GF→TTMn synapse, and the GF relies solely on the chemical synapse for signaling (Boerner et al., 2024). Gap junctions are naturally lost as the fly ages, so we set our limits of gap junction conductance following parameters found in Augustin et al. (2019) that follow age-related decreases in conductance. We added an exception for zero gap junctions, which led to zero conductance. We used a sigmoid equation to simplify the Boltzmann equation since we want to look at the effects of varying gap junction proportions on responses and not channel dynamics. D, We find that our model (dashed lines) can closely mimic the response decrement profile of our frazzled mutant flies (solid lines). Colors as in Figure 4.
Tables
- Table 1.
A KS2D2S test to identify differences between frazzled LOF mutants and LOF flies driving different UAS-Frazzled rescue constructs
KS2D2S Sample 1 (n = 35 terminals) Sample 2 Sample 2 Terminals p-value (Response Latency versus GJ%) p-value (Response Frequency versus GJ%) 1 frazzled LOF mutant (w; fra3/fra4; R91H05::GFP/+) frazzled heterozygous sibling (w; fra3/CyO; R91H05::GFP/+) n = 16 0.0001* 0.0001* 2 frazzled LOF mutant (w; fra3/fra4; R91H05::GFP/+) Full-length Frazzled (w; fra3/fra4; R91H05::GFP/UAS-Frazzled) n = 38 0.0373* 0.0076* 3 frazzled LOF mutant (w; fra3/fra4; R91H05::GFP/+) Frazzled’s intracellular domain (w; fra3/fra4; R91H05::GFP/UAS-FraICD) n = 23 0.0007* 0.0004* 4 frazzled LOF mutant (w; fra3/fra4; R91H05::GFP/+) Point-mutated Frazzled (w; fra3/fra4; R91H05::GFP/UAS-HA-FraE1354A) n = 13 0.0628 0.0404* 5 frazzled LOF mutant (w; fra3/fra4; R91H05::GFP/+) Frazzled P1 domain deletion (w; fra3/fra4; R91H05::GFP/UAS-HA-FraΔP1) n = 7 0.0013* 0.0153* 6 frazzled LOF mutant (w; fra3/fra4; R91H05::GFP/+) Frazzled P2 domain deletion (w; fra3/fra4; R91H05::GFP/UAS-HA-FraΔP2) n = 20 0.0002* 0.0054* 7 frazzled LOF mutant (w; fra3/fra4; R91H05::GFP/+) Frazzled P3 domain deletion (w; fra3/fra4; R91H05::GFP/UAS-HA-FraΔP3) n = 12 0.3085 0.5607 8 frazzled LOF mutant (w; fra3/fra4; R91H05::GFP/+) Wild-type unablated (w; + ; R91H05::GFP/10×-UAS-GFP) n = 8 0.0055* 0.0098* 9 frazzled LOF mutant (w; fra3/fra4; R91H05::GFP/+) Wild-type ablated (w; + ; R91H05::GFP/10×-UAS-GFP) n = 22 0.0282* 0.0009* 10 frazzled LOF mutant (w; fra3/fra4; R91H05::GFP/+) shak-B2 heterozygous sibling ablation (shak-B2/Fm6; + ; R91H05::GFP/R91H05::GFP) n = 14 0.3688* 0.0076* 11 frazzled LOF mutant (w; fra3/fra4; R91H05::GFP/+) shak-B2 ablation (shak-B2/shak-B2; + ; R91H05::GFP/R91H05::GFP) n = 10 0.0019* 0.0003* The individual, independent comparisons made with the KS2D2S test revealed whether there are differences in the distributions of response latency averages and the proportion of volume taken up by gap junction antibody in GF terminals, with significant differences shown by asterisks (*). The KS2D2S comparing response frequencies to the proportion of volume taken up by gap junction antibody in GF terminals shows the same significant differences as the latency test, except for UAS-FraE1354A (see results).
Data 1
Computational Model of the GFS. This Python (v3.12) code models the Giant Fiber system and the variable responses generated when changes to gap junction signaling are made. The code was developed in a Jupyter Notebooks environment (v7.3.2). Download Data 1, ZIP file.
Figure 1-1
HA- tag immunolabeling in a frazzled LOF mutant driving expression of UAS-HA-FraE1354A in the Giant Fibers. Figures show expression of UAS-HA-FraE1354A genetic construct. All panels are the same genotype. The top and bottom rows are different samples. A) Sample showing expression of GFP and anti-HA in the brain of Drosophila, with GFP in green and anti-HA in cyan. B) GFP expression channel. C) Anti-HA expression channel. D) Different sample showing expression of GFP and anti-HA in a single GF. E) GFP expression channel. F) Anti-HA expression channel. Download Figure 1-1, TIF file.
In this issue
